In today’s digital age, large language models (LLMs) have revolutionized the way we interact with and harness the power of artificial intelligence (AI). From answering complex questions to providing insightful explanations, LLMs have become indispensable tools for various domains. When it comes to mathematics, having an LLM specifically tailored to handle mathematical concepts and problem-solving can be immensely valuable.
In this article, we delve into the world of LLMs and explore the best options available for tackling math-related queries. These advanced language models have undergone rigorous training, incorporating vast amounts of mathematical knowledge and problem-solving capabilities. By understanding their strengths, unique features, and specific applications, we aim to provide readers with insights to make informed decisions when utilizing LLMs for mathematical tasks.
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WizardLM Mega
WizardLM Mega is an exceptional LLM program that showcases the culmination of advancements in AI language models. Built upon the foundations of the Llama 13B model, it has been meticulously fine-tuned using the extensive ShareGPT, WizardLM, and Wizard-Vicuna datasets. One notable feature of Wizard Mega is its rigorous filtering process, which ensures that responses lacking meaningful insights such as generic statements, e.g., “As an AI language model…” are eliminated. Moreover, the model actively avoids refusing to respond, enhancing its usability and reliability.
WizardLM Mega stands out from its counterparts by demonstrating remarkable proficiency in handling complex mathematical and programming tasks. Its versatility and robustness are evident as it consistently applies logical reasoning to problem-solving. This remarkable consistency is a testament to the extensive training and fine-tuning process that WizardLM Mega has undergone.
Wizard Vicuna
Wizard Vicuna is an advanced AI language model, specifically the wizard-vicuna-13b variant. It has been trained on a subset of data with responses that do not contain alignment or moralizing elements. The purpose of this training approach is to develop a WizardLM that does not have alignment built-in, allowing alignment to be added separately using methods like Reinforcement Learning from Human Feedback (RLHF) or the LoRA framework.
The development of Wizard Vicuna has been made possible thanks to the contributions and support of the open-source AI/ML community. This model aims to provide a more open-ended and flexible conversational experience, free from the constraints of predefined alignment. It allows users to add their own alignment preferences or ethical considerations as needed.
It’s important to note that Wizard Vicuna, like any uncensored model, lacks guardrails or predefined limitations. Users should exercise responsibility when interacting with the model.
StableVicuna-13B
StableVicuna-13B is an advanced LLM (Language Model for Math) built on the foundation of the powerful Vicuna-13B v0 model. It has been fine-tuned using reinforcement learning from human feedback (RLHF) through Proximal Policy Optimization (PPO) techniques, leveraging various conversational and instructional datasets.
One notable feature of StableVicuna-13B is its ability to apply delta weights. While using the CarperAI/stable-vicuna-13b-delta weights alone is not sufficient, by incorporating the difference between LLaMA 13B and CarperAI/stable-vicuna-13b-delta weights, users can access the correct model. To streamline this conversion process, an apply_delta.py script is provided, allowing users to automate the conversion with ease.
StableVicuna-13B’s fine-tuning is based on a combination of three datasets. The first is the OpenAssistant Conversations Dataset (OASST1), which comprises a vast collection of human-generated, human-annotated assistant-style conversations. With 161,443 messages distributed across 66,497 conversation trees, this corpus covers a diverse range of topics and is available in 35 different languages. The second dataset used is the GPT4All Prompt Generations, containing 400k prompts and their corresponding responses generated by the GPT-4 model.
While it’s important to note that StableVicuna-13B may not excel specifically in mathematical problem-solving as compared to some of the higher-ranked AI models on the list, it still offers significant improvements over other LLMs.
